import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import random
import os
import shutil
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
from sklearn.decomposition import PCA
from xgboost import XGBClassifier
from sklearn.metrics import f1_score, confusion_matrix, classification_report, roc_auc_score, roc_curve
from tensorflow.keras import models, layers
from tensorflow.keras.utils import plot_model
from tensorflow.keras.optimizers import Adam, SGD, RMSprop, Adagrad, Adadelta, Nadam, Ftrl
from keras.wrappers.scikit_learn import KerasClassifier
#from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
import keras_tuner as kt
from keras_tuner import Objective
from tensorflow.keras.callbacks import EarlyStopping

import warnings
warnings.filterwarnings('ignore')

# Fijar semillas
np.random.seed(42)
tf.random.set_seed(42)
random.seed(42)

# Configuraciones estéticas
sns.set(style="whitegrid")
plt.rcParams["figure.figsize"] = (12, 6)

# Cargar archivos (rutas relativas)
train = pd.read_csv("../data/raw/train_data.csv")
test = pd.read_csv("../data/raw/test_data.csv")
sample_submission = pd.read_csv("../data/raw/sampleSubmission.csv")

# Tamaño de los datasets
print("Tamaño Train:", train.shape)
print("Tamaño Test:", test.shape)

Tamaño Train: (2050, 65)
Tamaño Test: (500, 64)

train

test.head(5)

# Tipos de datos y valores nulos
# train.info()

# Estadísticas descriptivas
train.describe().T

# Distribuciones de variables
columns_f = [col for col in train.columns if col != 'ID']

# Crear una figura con subplots
fig, axes = plt.subplots(nrows=8, ncols=8, figsize=(30, 30))
axes = axes.flatten()

# Graficar los histogramas para cada variable
for i, column in enumerate(columns_f):
    sns.histplot(train[column], kde=False, ax=axes[i], bins=20, color="#dcbb65")
    axes[i].set_title(column)

# Ajustar los márgenes para que las etiquetas no se solapen
plt.tight_layout()
plt.show()

# Distribución de la variable objetivo
plt.figure(figsize=(6, 5))  
sns.countplot(x='Bankruptcy', hue='Bankruptcy', data=train, palette='RdBu', legend=False)
plt.title('Distribución de la variable objetivo')
plt.show()

# Porcentaje de casos positivos (bancarrota)
bankrupt_rate = train['Bankruptcy'].mean()
print(f"Tasa de bancarrota: {bankrupt_rate:.2%}")

Tasa de bancarrota: 3.02%

# Porcentaje de valores nulos por columna
nulls = train.isnull().mean().sort_values(ascending=False)
nulls[nulls > 0]

Series([], dtype: float64)

# Validar duplicados exactos (todas las columnas)
duplicados = train.duplicated()
print(f"Número de registros duplicados: {duplicados.sum()}")

Número de registros duplicados: 0

# Selección de variables numéricas (excluyendo la variable objetivo)
variables_numericas = train.drop(columns=['Bankruptcy', 'Net.Income.Flag']).select_dtypes(include='number').columns

# Función para detectar valores atípicos usando el rango intercuartílico (IQR)
def detectar_outliers_iqr(df, col):
    Q1 = df[col].quantile(0.25)
    Q3 = df[col].quantile(0.75)
    IQR = Q3 - Q1
    outliers = df[(df[col] < Q1 - 1.5 * IQR) | (df[col] > Q3 + 1.5 * IQR)]
    return outliers.shape[0]

# Número de valores atípicos por columna
outliers_por_col = {col: detectar_outliers_iqr(train, col) for col in variables_numericas}
outliers_df = pd.DataFrame.from_dict(outliers_por_col, orient='index', columns=['N_outliers'])
outliers_df.sort_values(by='N_outliers', ascending=False)

# Mapa de calor de correlaciones
plt.figure(figsize=(45, 35))
corr_matrix = train.drop(columns=['ID', 'Net.Income.Flag']).corr()
sns.heatmap(corr_matrix, cmap="coolwarm", annot=True, fmt=".1f", cbar=True, 
            xticklabels=corr_matrix.columns, yticklabels=corr_matrix.columns, 
            square=True, linewidths=0.5)

plt.xticks(rotation=45, ha="right", fontsize=16) 
plt.yticks(rotation=0, fontsize=16)
plt.title("Mapa de calor de correlaciones", fontsize=28)
plt.tight_layout()
plt.show()

# Matriz de correlación alta (superior a 0.90)
cor_matrix = train[variables_numericas].corr().abs()
alta_cor = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(bool))
pares_altamente_correlacionados = [(col, alta_cor[col].idxmax(), alta_cor[col].max()) 
                                   for col in alta_cor.columns if alta_cor[col].max() > 0.90]

print("Pares de variables altamente correlacionadas (> 0.90):")
for par in pares_altamente_correlacionados:
    print(f"{par[0]} y {par[1]} — Correlación: {par[2]:.2f}")

Pares de variables altamente correlacionadas (> 0.90):
No.credit.Interval y Realized.Sales.Gross.Profit.Growth.Rate — Correlación: 0.95
Working.Capital.Equity y Continuous.Net.Profit.Growth.Rate — Correlación: 0.97
Pre.tax.net.Interest.Rate y Inventory.Working.Capital — Correlación: 0.99
After.tax.Net.Profit.Growth.Rate y Net.Value.Per.Share..B. — Correlación: 0.91
Inventory.and.accounts.receivable.Net.value y Current.Liabilities.Equity — Correlación: 1.00
Continuous.interest.rate..after.tax. y Persistent.EPS.in.the.Last.Four.Seasons — Correlación: 0.93
Operating.Gross.Margin y Operating.profit.per.person — Correlación: 0.93
Operating.Profit.Growth.Rate y Interest.Coverage.Ratio..Interest.expense.to.EBIT. — Correlación: 0.98
Cash.Flow.to.Liability y Current.Liability.to.Equity — Correlación: 0.92

# Calcular la correlación con la variable 'Bankruptcy' y ordenar las variables
cor_target = train.corr()["Bankruptcy"].sort_values(ascending=False)
cor_target.drop(['ID', 'Bankruptcy', 'Net.Income.Flag'], inplace=True)

# Crear los subplots
fig, axes = plt.subplots(1, 2, figsize=(16, 4))  # 1 fila, 2 columnas

# Subgráfico 1: Las 5 variables más correlacionadas positivamente con 'Bankruptcy'
cor_target.head(5).plot(kind='barh', ax=axes[0], color='skyblue')
axes[0].set_title('Top 5 Variables Más Correlacionadas Positivamente con Bankruptcy')
axes[0].set_xlabel('Correlación con Bankruptcy')
axes[0].set_ylabel('Variables')

# Subgráfico 2: Las 5 variables más correlacionadas negativamente con 'Bankruptcy'
cor_target.tail(5).plot(kind='barh', ax=axes[1], color='lightcoral')
axes[1].set_title('Top 5 Variables Más Correlacionadas Negativamente con Bankruptcy')
axes[1].set_xlabel('Correlación con Bankruptcy')
axes[1].set_ylabel('Variables')

plt.tight_layout()
plt.show()

def extend_features(df): 
    # RATIOS FINANCIEROS
    # Z-score modificado de Altman
    df['Z_score_modificado'] = (
        1.2 * df['Working.Capital.to.Total.Assets'] +
        1.4 * df['Retained.Earnings.to.Total.Assets'] +
        3.3 * df['Operating.Profit.Rate'] +
        0.6 * df['Equity.to.Liability'] +
        1.0 * df['Revenue.per.person']  # Ajustado como proxy de ventas/activos
    )

    # Capital de trabajo neto sobre activos totales
    df['Capital_Trabajo_Ratio'] = (df['Current.Assets.Total.Assets'] - df['Current.Liability.to.Assets'])

    # INDICADORES DE SOLVENCIA
    # Apalancamiento extendido
    df['Apalancamiento_Extendido'] = df['Total.debt.Total.net.worth'] + df['Contingent.liabilities.Net.worth']

    # Ratio de cobertura de obligaciones financieras
    df['Cobertura_Deuda'] = df['Operating.Profit.Rate'] / (df['Interest.bearing.debt.interest.rate'] + 1e-6)

    # Indicador de estrés financiero: pasivos corrientes vs activos líquidos
    df['Stress_Financiero'] = df['Current.Liability.to.Current.Assets'] / (df['Cash.Total.Assets'] + 1e-6)

    # Relación de liquidez con obligaciones: flujo de caja vs pasivos
    df['Liquidez_vs_Pasivos'] = df['Cash.Flow.to.Liability'] - df['Interest.Coverage.Ratio..Interest.expense.to.EBIT.']

    # EFICIENCIA OPERATIVA
    # Cambio en margen bruto vs margen operativo (eficiencia operativa)
    df['Cambio_Margen_Bruto'] = df['Gross.Profit.to.Sales'] - df['Operating.Gross.Margin']

    # Bandera de alerta si flujo operativo es bajo y ratio de deuda alto
    df['Bandera_Bajo_Flujo_Operativo'] = np.where(
        (df['Cash.Flow.to.Sales'] < 0.05) & (df['Debt.ratio..'] > 0.7),
        1, 0) 

    # Margen de beneficio después de impuestos
    df['Margen_Neto_Post_Impuestos'] = df['After.tax.Net.Profit.Growth.Rate'] / (df['Operating.Profit.Rate'] + 1e-6)

    # VOLATILIDAD
    # Variación de utilidad por acción entre temporadas (estabilidad)
    df['Volatilidad_EPS'] = df['Persistent.EPS.in.the.Last.Four.Seasons'].rolling(window=4, min_periods=1).std()
    
    return df

# Agregar nuevas variables
train = extend_features(train)
test = extend_features(test)

# Porcentaje de valores nulos por columna dataset de train
nulls = train.isnull().mean().sort_values(ascending=False)
print(nulls[nulls > 0])

# Porcentaje de valores nulos por columna dataset de test
nulls = test.isnull().mean().sort_values(ascending=False)
print(nulls[nulls > 0])

Volatilidad_EPS    0.000488
dtype: float64
Volatilidad_EPS    0.002
dtype: float64

# Separar features y target para que al imputar tengan las mismas columnas
X = train.drop(columns=["Bankruptcy", "Net.Income.Flag", "ID"])
y = train["Bankruptcy"]

# Guardar ID del test para el submission
test_ids = test["ID"]
X_test = test.drop(columns=["Net.Income.Flag", "ID"])
X_test = X_test.reindex(columns=X.columns, fill_value=np.nan)

# Crear el imputer usando la mediana
median_imputer = SimpleImputer(strategy='median')

# Aplicar el imputer a los dataset de train y test
X_train_imputed = pd.DataFrame(median_imputer.fit_transform(X), columns=X.columns)
X_test_imputed = pd.DataFrame(median_imputer.transform(X_test), columns=X.columns)

# Dividir dataset de train en entrenamiento y validación
X_train, X_val, y_train, y_val = train_test_split(X_train_imputed, y, test_size=0.2, stratify=y, random_state=42)

# Escalar los datos
scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled = scaler.transform(X_val)
X_test_scaled = scaler.transform(X_test_imputed)

# Aplicar SMOTE para balancear clases en el conjunto de entrenamiento
smote = SMOTE(random_state=42)
X_train_bal, y_train_bal = smote.fit_resample(X_train_scaled, y_train)

# Inicializamos PCA
pca = PCA(n_components=0.97)

# Ajuste y transformación de los datos de entrenamiento
X_train_pca = pca.fit_transform(X_train_scaled)

# Transformación los datos de validación y prueba
X_val_pca = pca.transform(X_val_scaled)
X_test_pca = pca.transform(X_test_scaled)

def importance_XGBoost(X_train, y_train):
    # Entrenar el modelo
    xgb_model = XGBClassifier(random_state=42, tree_method="hist", device="cuda", eval_metric='logloss')
    xgb_model.fit(X_train, y_train)

    # Obtener la importancia por 'weight' o pesos
    feature_important = xgb_model.get_booster().get_score(importance_type='weight')

    # Extraer claves (f0, f1, etc) y valores
    keys = list(feature_important.keys())
    values = list(feature_important.values())

    # Crear el DataFrame de importancia
    importance_df = pd.DataFrame({
        'feature_code': keys,
        'weight_importance': values
    })

    # Mapear de f0, f1, ... a los nombres reales de las columnas
    feature_map = {f"f{i}": col for i, col in enumerate(X.columns)}
    importance_df['feature_name'] = importance_df['feature_code'].map(feature_map)

    # Ordenar por importancia
    importance_df = importance_df.sort_values(by='weight_importance', ascending=False)

    return importance_df, xgb_model

importance_df, xgb_model = importance_XGBoost(X_train_scaled, y_train)
importance_df_bal, xgb_model_bal = importance_XGBoost(X_train_bal, y_train_bal)

# Filtrar las variables que superan el umbral de 5 de importancia -> Sin balanceo
threshold = 5
selected_features = importance_df[importance_df['weight_importance'] > threshold]['feature_name'].tolist()
print(f"Número de variables seleccionadas sin SMOTE: {len(selected_features)}")

Número de variables seleccionadas sin SMOTE: 40

# Filtrar las variables que superan el umbral de 9 de importancia -> Con balanceo
threshold_bal = 9
selected_features_bal = importance_df_bal[importance_df_bal['weight_importance'] > threshold_bal]['feature_name'].tolist()
print(f"Número de variables seleccionadas con SMOTE {len(selected_features_bal)}")

Número de variables seleccionadas con SMOTE 42

X_train_scaled_df = pd.DataFrame(X_train_scaled, columns=X_train_imputed.columns)
X_val_scaled_df = pd.DataFrame(X_val_scaled, columns=X_train_imputed.columns)

# Subconjunto de datos con esas variables
X_train_sub = X_train_scaled_df[selected_features]
X_valid_sub = X_val_scaled_df[selected_features]

X_train_scaled_df_bal = pd.DataFrame(X_train_bal, columns=X_train_imputed.columns)
X_val_scaled_df_bal = pd.DataFrame(X_val_scaled, columns=X_train_imputed.columns)

# Subconjunto de datos con esas variables
X_train_sub_bal = X_train_scaled_df_bal[selected_features_bal]
X_valid_sub_bal = X_val_scaled_df_bal[selected_features_bal]

# y_train_bal

# X_train_pca
# X_val_pca

X_train_pca_bal, y_train_bal = smote.fit_resample(X_train_pca, y_train)
# X_val_pca

def plot_training_history(history):
    # Graficar la pérdida (Loss)
    plt.figure(figsize=(12,5))

    plt.subplot(1,2,1)
    plt.plot(history.history['loss'], label='Train Loss')
    plt.plot(history.history['val_loss'], label='Validation Loss')
    plt.title('Evolución de la función de pérdida')
    plt.xlabel('Época')
    plt.ylabel('Pérdida')
    plt.legend()

    # Graficar el AUC
    plt.subplot(1,2,2)
    plt.plot(history.history['auc'], label='Train AUC')
    plt.plot(history.history['val_auc'], label='Validation AUC')
    plt.title('Evolución del AUC')
    plt.xlabel('Época')
    plt.ylabel('AUC')
    plt.legend()

    plt.tight_layout()
    plt.show()

def evaluacion_modelo(history):
    # Extraer las métricas de history
    train_loss = history.history['loss']
    val_loss = history.history['val_loss']
    train_accuracy = history.history['accuracy']
    val_accuracy = history.history['val_accuracy']
    train_precision = history.history['precision']
    val_precision = history.history['val_precision']
    train_recall = history.history['recall']
    val_recall = history.history['val_recall']
    train_auc = history.history['auc']
    val_auc = history.history['val_auc']
    
    # Función para calcular F1-score a partir de Precision y Recall
    def calcular_f1(precision, recall):
        if (precision + recall) != 0:
            return 2 * (precision * recall) / (precision + recall)
        return 0  # Si Precision + Recall es 0, devolver 0 (o usar otro valor según lo que prefieras)

    # Usamos el último valor (última época) de cada métrica para el cálculo del F1-score
    f1_train = calcular_f1(train_precision[-1], train_recall[-1])
    f1_val = calcular_f1(val_precision[-1], val_recall[-1])

    # Crear df con resultados
    metrics_data = {
        'Train': [train_loss[-1], train_accuracy[-1], train_precision[-1], train_recall[-1], train_auc[-1], f1_train],
        'Validation': [val_loss[-1], val_accuracy[-1], val_precision[-1], val_recall[-1], val_auc[-1], f1_val]
    }
    
    df_metrics = pd.DataFrame(metrics_data, index=['Loss', 'Accuracy', 'Precision', 'Recall', 'AUC', 'F1-score'])
    df_metrics = df_metrics.round(2)
    
    return df_metrics, val_loss[-1], val_auc[-1], val_precision[-1], val_recall[-1], f1_val

# Definir modelo base
model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(X_train_sub.shape[1],)),
    layers.Dense(32, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

# Compilar modelo
model.compile(optimizer=Adam(),
              loss='binary_crossentropy',
              metrics=['accuracy', 'Precision', 'Recall', 'AUC'])

history = model.fit(
    X_train_sub, y_train,
    validation_data=(X_valid_sub, y_val),
    epochs=50, 
    batch_size=32,
    verbose=0
)

plot_training_history(history)
df_metrics_base, val_loss_base, val_auc_base, val_precision_base, val_recall_base, f1_val_base = evaluacion_modelo(history)
print(val_loss_base, val_auc_base, val_precision_base, val_recall_base, f1_val_base)
df_metrics_base

0.2691952586174011 0.685092031955719 0.375 0.25 0.3

# Definir modelo base con regularización L2 y Dropout
model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(X_train_sub.shape[1],), 
                 kernel_regularizer=tf.keras.regularizers.l2(0.01)),  # L2 regularization
    layers.Dropout(0.3),  # Dropout regularization
    layers.Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)),  # L2 regularization
    layers.Dropout(0.3),  # Dropout regularization
    layers.Dense(1, activation='sigmoid')
])

# Compilar modelo
model.compile(optimizer=Adam(),
              loss='binary_crossentropy',
              metrics=['accuracy', 'Precision', 'Recall', 'AUC'])

history = model.fit(
    X_train_sub, y_train,
    validation_data=(X_valid_sub, y_val),
    epochs=50, 
    batch_size=32,
    verbose=0
)

plot_training_history(history)
df_metrics_base_reg, val_loss_base_reg, val_auc_base_reg, val_precision_base_reg, val_recall_base_reg, f1_val_base_reg = evaluacion_modelo(history)
print(val_loss_base_reg, val_auc_base_reg, val_precision_base_reg, val_recall_base_reg, f1_val_base_reg)
df_metrics_base_reg

0.10754568874835968 0.9216917157173157 0.5 0.0833333358168602 0.1428571465064068

# Mejor modelo con ajuste de hiperparámetros manual
model_manual= models.Sequential([
    layers.Dense(40, activation='gelu', input_shape=(X_train_sub.shape[1],),
                 kernel_regularizer=tf.keras.regularizers.l2(0.01)),
    layers.Dropout(0.8),
    layers.Dense(20, activation='silu', kernel_regularizer=tf.keras.regularizers.l2(0.005)),
    layers.Dropout(0.8),
    layers.Dense(1, activation='sigmoid')
])

# Compilar modelo
model_manual.compile(optimizer=Adam(learning_rate=0.001),
              loss='binary_crossentropy',
              metrics=['accuracy', 'Precision', 'Recall', 'AUC'])

history_manual = model_manual.fit(
    X_train_sub, y_train,
    validation_data=(X_valid_sub, y_val),
    epochs=100, 
    batch_size=32,
    verbose=0
)

plot_training_history(history_manual)
df_metrics_man, val_loss_man, val_auc_man, val_precision_man, val_recall_man, f1_val_man = evaluacion_modelo(history_manual)
print(val_loss_man, val_auc_man, val_precision_man, val_recall_man, f1_val_man)
df_metrics_man

0.11583803594112396 0.9125838279724121 0.0 0.0 0

# Definir la función de construcción del modelo
def build_keras_base(hidden_layers=[40, 20], dropout_rate=0.8, optimizer='adam', learning_rate=0.001):
#def build_keras_base(hidden_layers=[40, 20], dropout_rate=0.8, optimizer='adam', learning_rate=0.001):
    model_opt = models.Sequential()

    # Primera capa densa con regularización L2
    model_opt.add(layers.Dense(hidden_layers[0], activation='gelu', input_shape=(X_train_sub.shape[1],),
                               kernel_regularizer=tf.keras.regularizers.l2(0.01)))
    model_opt.add(layers.Dropout(dropout_rate))

    # Segunda capa densa con regularización L2
    model_opt.add(layers.Dense(hidden_layers[1], activation='silu', 
                               kernel_regularizer=tf.keras.regularizers.l2(0.005)))
    model_opt.add(layers.Dropout(dropout_rate))

    # Capa de salida con activación sigmoide
    model_opt.add(layers.Dense(1, activation='sigmoid'))

    # Crear el optimizador basado en el nombre pasado
    if optimizer == 'adam':
        optimizer_instance = Adam(learning_rate=learning_rate)
    elif optimizer == 'sgd':
        optimizer_instance = SGD(learning_rate=learning_rate)
    elif optimizer == 'rmsprop':
        optimizer_instance = RMSprop(learning_rate=learning_rate)
    elif optimizer == 'adagrad':
        optimizer_instance = Adagrad(learning_rate=learning_rate)
    elif optimizer == 'adadelta':
        optimizer_instance = Adadelta(learning_rate=learning_rate)
    elif optimizer == 'nadam':
        optimizer_instance = Nadam(learning_rate=learning_rate)
    elif optimizer == 'ftrl':
        optimizer_instance = Ftrl(learning_rate=learning_rate)
    else:
        raise ValueError(f"Optimizer {optimizer} no reconocido.")

    # Compilación del modelo
    model_opt.compile(optimizer=optimizer_instance,
                      loss='binary_crossentropy',
                      metrics=['accuracy', 'Precision', 'Recall', 'AUC'])

    return model_opt

# Configurar el KerasClassifier
model_keras = KerasClassifier(build_fn=build_keras_base, verbose=0)
model_keras._estimator_type = "classifier"

# Parámetros para la búsqueda de hiperparámetros
hidden_layers_opt = [[40, 20], [40, 15], [50, 25]]
batch_size_opt = [16, 32]
epochs_opt = [30, 60, 100, 120]
optimizer = ['adam', 'sgd', 'rmsprop', 'adagrad', 'adadelta', 'nadam', 'ftrl']
learning_rate_opt = [0.001, 0.01, 0.1]

parameters_opt = {
    'hidden_layers': hidden_layers_opt,
    'optimizer': optimizer,
    'batch_size': batch_size_opt,
    'epochs': epochs_opt,
    'learning_rate': learning_rate_opt
}

rnd_search = RandomizedSearchCV(model_keras,
                                param_distributions=parameters_opt, 
                                scoring='roc_auc',
                                n_iter=10,
                                cv=10,
                                verbose=1)

# Realizar la búsqueda
rnd_result = rnd_search.fit(X_train_sub, y_train)

Fitting 10 folds for each of 10 candidates, totalling 100 fits

# Mejor modelo con ajuste de hiperparámetros randomized search
best_model = rnd_search.best_estimator_

best_params = rnd_search.best_params_
print("Mejores parámetros encontrados:", best_params)

Mejores parámetros encontrados: {'optimizer': 'nadam', 'learning_rate': 0.001, 'hidden_layers': [50, 25], 'epochs': 100, 'batch_size': 16}

# Ejecución del mejor modelo con ajuste de hiperparámetros randomized search
model_rnd= models.Sequential([
    layers.Dense(50, activation='gelu', input_shape=(X_train_sub.shape[1],),
                 kernel_regularizer=tf.keras.regularizers.l2(0.01)),
    layers.Dropout(0.8),
    layers.Dense(25, activation='silu', kernel_regularizer=tf.keras.regularizers.l2(0.005)),
    layers.Dropout(0.8),
    layers.Dense(1, activation='sigmoid')
])

# Compilar modelo
model_rnd.compile(optimizer=Nadam(learning_rate=0.001),
              loss='binary_crossentropy',
              metrics=['accuracy', 'Precision', 'Recall', 'AUC'])

history_rnd = model_rnd.fit(
    X_train_sub, y_train,
    validation_data=(X_valid_sub, y_val),
    epochs=100, 
    batch_size=16,
    verbose=0
)

# Evaluación del mejor modelo con ajuste de hiperparámetros randomized search
plot_training_history(history_rnd)
df_metrics_rnd, val_loss_rnd, val_auc_rnd, val_precision_rnd, val_recall_rnd, f1_val_rnd = evaluacion_modelo(history_rnd)
print(val_loss_rnd, val_auc_rnd, val_precision_rnd, val_recall_rnd, f1_val_rnd)
df_metrics_rnd

0.11519674211740494 0.9068257808685303 0.0 0.0 0

# Búsqueda en cuadrícula
grid_search = GridSearchCV(estimator=model_keras, 
                           param_grid=parameters_opt,
                           cv=5,
                           verbose=2)

# Realizamos la búsqueda de los mejores hiperparámetros
grid_search_result = grid_search.fit(X_train_sub, y_train)

# Mostramos los mejores resultados
print(f"Mejores parámetros: {grid_search_result.best_params_}")
print(f"Mejor puntuación: {grid_search_result.best_score_}")

Fitting 5 folds for each of 504 candidates, totalling 2520 fits
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   5.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   5.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   5.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   5.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   5.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.1s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   5.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.2s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.6s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   4.9s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   4.8s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.3s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   5.0s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   8.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   8.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   9.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   8.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.7s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   8.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.6s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.4s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.5s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   6.9s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   8.0s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.2s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.8s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=16, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  11.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  13.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  13.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  12.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  11.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  13.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  12.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  10.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  12.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  10.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  11.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  12.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  12.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  12.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  12.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  11.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  12.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  12.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  12.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  11.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  11.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  13.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  12.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  12.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  12.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  11.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  11.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  10.6s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  11.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  10.8s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  10.9s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  10.7s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  11.1s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  11.0s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  12.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  12.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  13.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  12.4s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  11.5s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  11.2s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  11.3s
[CV] END batch_size=16, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  11.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  14.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  14.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=  14.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  15.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=  14.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  12.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  15.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  12.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  15.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  14.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  12.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=  14.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  13.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  12.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  14.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  15.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  15.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=  14.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  12.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  13.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  14.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  15.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  15.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=  13.3s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  13.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  14.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=  14.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  14.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  12.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  12.8s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  12.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=  13.9s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  13.0s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=  13.1s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  14.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  14.5s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  15.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  14.7s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  14.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  13.6s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  13.2s
[CV] END batch_size=16, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=  13.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   3.2s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   3.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   2.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   3.2s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   3.2s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   3.2s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   2.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   2.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   2.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   4.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   3.1s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   3.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   2.7s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   3.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   2.8s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   3.3s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   4.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   3.4s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   3.0s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   2.9s
[CV] END batch_size=32, epochs=30, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   5.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   5.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   6.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   5.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   5.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   5.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   5.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   6.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   5.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   5.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   5.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   6.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   5.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   5.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   5.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   5.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   6.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   5.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   5.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   5.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.9s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   4.9s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.8s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   5.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   4.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   5.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   5.0s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   6.3s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   5.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   4.7s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   5.9s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   4.4s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   5.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.2s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   5.1s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.6s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   4.5s
[CV] END batch_size=32, epochs=60, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   5.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   7.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   7.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   7.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   7.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   8.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   8.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   8.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   8.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   6.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.0s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   6.8s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   6.3s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   6.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   6.7s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   6.6s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   7.4s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   7.5s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.2s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.1s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=100, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   6.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   9.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=nadam; total time=  10.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   8.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=adadelta; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   9.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.01, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adam; total time=   9.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=  10.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 20], learning_rate=0.1, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   9.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adam; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=sgd; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=rmsprop; total time=   9.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=  10.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.001, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   9.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=sgd; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=rmsprop; total time=   9.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=  10.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.01, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   9.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adam; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=sgd; total time=   7.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   9.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adagrad; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   9.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=nadam; total time=   8.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[40, 15], learning_rate=0.1, optimizer=ftrl; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   9.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=rmsprop; total time=   9.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=adadelta; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=  10.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   9.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.001, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   9.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=rmsprop; total time=   9.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adagrad; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=  10.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=nadam; total time=   9.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   8.0s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.01, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   9.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   8.3s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adam; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=sgd; total time=   7.5s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=rmsprop; total time=   9.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adagrad; total time=   7.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=adadelta; total time=   7.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.6s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.7s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=  10.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=nadam; total time=   8.9s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.4s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.1s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.2s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   7.8s
[CV] END batch_size=32, epochs=120, hidden_layers=[50, 25], learning_rate=0.1, optimizer=ftrl; total time=   8.0s
Mejores parámetros: {'batch_size': 16, 'epochs': 100, 'hidden_layers': [50, 25], 'learning_rate': 0.1, 'optimizer': 'adagrad'}
Mejor puntuación: 0.9713414907455444

print(f"Mejores parámetros: {grid_search_result.best_params_}")
print(f"Mejor puntuación: {grid_search_result.best_score_}")

Mejores parámetros: {'batch_size': 16, 'epochs': 100, 'hidden_layers': [50, 25], 'learning_rate': 0.1, 'optimizer': 'adagrad'}
Mejor puntuación: 0.9713414907455444

# Mejor modelo con ajuste de hiperparámetros Grid Search
model_best_GS = build_keras_base(hidden_layers=[50, 25],
    dropout_rate=0.8,
    optimizer='adagrad',
    learning_rate=0.1
)


# Entrenar el modelo y capturar el history
history_GS = model_best_GS.fit(X_train_sub, y_train,
                         batch_size=16,
                         epochs=100,
                         verbose=0,
                         validation_data=(X_valid_sub, y_val))

# Evaluación del mejor modelo con ajuste de hiperparámetros Grid Search
plot_training_history(history_GS)
df_metrics_GS, val_loss_GS, val_auc_GS, val_precision_GS, val_recall_GS, f1_val_GS = evaluacion_modelo(history_GS)
print(val_loss_GS, val_auc_GS, val_precision_GS, val_recall_GS, f1_val_GS)
df_metrics_GS

0.11414438486099243 0.915724515914917 0.0 0.0 0

# Definir el EarlyStopping callback
early_stopping = EarlyStopping(monitor='val_auc', patience=5, restore_best_weights=True)

def busqueda_hiperparams_tuner(X_train, y_train, X_val, y_val):
    # Función para construir el modelo
    def build_keras_base(hp):
        model_opt = models.Sequential()

        # Primera capa densa con regularización L2
        model_opt.add(layers.Dense(
            hp.Int('units_1', min_value=16, max_value=64, step=16),
            activation='gelu',
            input_shape=(X_train.shape[1],),
            kernel_regularizer=tf.keras.regularizers.l2(0.01)
        ))
        model_opt.add(layers.Dropout(rate=hp.Float('dropout_1', min_value=0.2, max_value=0.9, step=0.1)))

        # Segunda capa densa
        model_opt.add(layers.Dense(
            hp.Int('units_2', min_value=16, max_value=64, step=16),
            activation='silu',
            kernel_regularizer=tf.keras.regularizers.l2(0.005)
        ))
        model_opt.add(layers.Dropout(rate=hp.Float('dropout_2', min_value=0.2, max_value=0.9, step=0.1)))

        # Capa de salida
        model_opt.add(layers.Dense(1, activation='sigmoid'))

        # Seleccionar el optimizador
        optimizer_choice = hp.Choice('optimizer', values=['adam', 'adagrad', 'adadelta', 'nadam', 'ftrl'])

        if optimizer_choice == 'adam':
            optimizer_instance = Adam(learning_rate=hp.Float('learning_rate', min_value=0.001, max_value=0.1, sampling='log'))
        elif optimizer_choice == 'adagrad':
            optimizer_instance = Adagrad(learning_rate=hp.Float('learning_rate', min_value=0.001, max_value=0.1, sampling='log'))
        elif optimizer_choice == 'adadelta':
            optimizer_instance = Adadelta(learning_rate=hp.Float('learning_rate', min_value=0.001, max_value=0.1, sampling='log'))
        elif optimizer_choice == 'nadam':
            optimizer_instance = Nadam(learning_rate=hp.Float('learning_rate', min_value=0.001, max_value=0.1, sampling='log'))
        elif optimizer_choice == 'ftrl':
            optimizer_instance = Ftrl(learning_rate=hp.Float('learning_rate', min_value=0.001, max_value=0.1, sampling='log'))

        # Compilar el modelo
        model_opt.compile(optimizer=optimizer_instance,
                          loss='binary_crossentropy',
                          metrics=['accuracy', 'Precision', 'Recall', 'AUC'])
        
        return model_opt

    # Configuración del tuner
    directory = os.path.abspath('kt_dir')
    project_name = 'model_tuning'
    path_to_delete = os.path.join(directory, project_name)
    if os.path.exists(path_to_delete):
        shutil.rmtree(path_to_delete)

    tuner = kt.Hyperband(
        build_keras_base,
        objective='val_loss',
        max_epochs=40,
        factor=3,
        directory=directory,
        project_name=project_name
    )

    # Búsqueda de hiperparámetros
    tuner.search(X_train, y_train, epochs=40, validation_data=(X_val, y_val))

    # Obtener los mejores hiperparámetros
    best_hyperparameters = tuner.get_best_hyperparameters(num_trials=1)[0]
    print(f"Mejores hiperparámetros encontrados: {best_hyperparameters.values}")

    # Construir y entrenar el mejor modelo
    best_model_tuner = tuner.hypermodel.build(best_hyperparameters)
    history_tuner = best_model_tuner.fit(X_train, y_train, epochs=40, validation_data=(X_val, y_val))

    return best_model_tuner, history_tuner

best_model_tuner_D1, history_turner_D1 = busqueda_hiperparams_tuner(X_train_sub, y_train, X_valid_sub, y_val)

Trial 90 Complete [00h 00m 08s]
val_loss: 1.2301617860794067

Best val_loss So Far: 0.10076415538787842
Total elapsed time: 00h 04m 49s
Mejores hiperparámetros encontrados: {'units_1': 16, 'dropout_1': 0.2, 'units_2': 32, 'dropout_2': 0.4, 'optimizer': 'adam', 'learning_rate': 0.0024335509762462414, 'tuner/epochs': 40, 'tuner/initial_epoch': 14, 'tuner/bracket': 1, 'tuner/round': 1, 'tuner/trial_id': '0076'}
Epoch 1/40
52/52 [==============================] - 2s 11ms/step - loss: 0.7395 - accuracy: 0.8726 - precision: 0.0182 - recall: 0.0600 - auc: 0.3896 - val_loss: 0.4756 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.5567
Epoch 2/40
52/52 [==============================] - 0s 2ms/step - loss: 0.4125 - accuracy: 0.9689 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6179 - val_loss: 0.3191 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7648
Epoch 3/40
52/52 [==============================] - 0s 2ms/step - loss: 0.2971 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7729 - val_loss: 0.2482 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8474
Epoch 4/40
52/52 [==============================] - 0s 2ms/step - loss: 0.2298 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.8789 - val_loss: 0.2043 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8817
Epoch 5/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1902 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.8955 - val_loss: 0.1759 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8836
Epoch 6/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1627 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9064 - val_loss: 0.1542 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8987
Epoch 7/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1482 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9052 - val_loss: 0.1420 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9028
Epoch 8/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1333 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9362 - val_loss: 0.1341 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9059
Epoch 9/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1279 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9160 - val_loss: 0.1267 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9102
Epoch 10/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1188 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9454 - val_loss: 0.1233 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9135
Epoch 11/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1170 - accuracy: 0.9695 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9345 - val_loss: 0.1189 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9165
Epoch 12/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1162 - accuracy: 0.9701 - precision: 1.0000 - recall: 0.0200 - auc: 0.9180 - val_loss: 0.1177 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9160
Epoch 13/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1134 - accuracy: 0.9707 - precision: 1.0000 - recall: 0.0400 - auc: 0.9215 - val_loss: 0.1154 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9068
Epoch 14/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1109 - accuracy: 0.9701 - precision: 0.6667 - recall: 0.0400 - auc: 0.9385 - val_loss: 0.1126 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9217
Epoch 15/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1092 - accuracy: 0.9720 - precision: 0.8333 - recall: 0.1000 - auc: 0.9197 - val_loss: 0.1106 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9231
Epoch 16/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1029 - accuracy: 0.9738 - precision: 0.8889 - recall: 0.1600 - auc: 0.9375 - val_loss: 0.1108 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9117
Epoch 17/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9701 - precision: 0.6000 - recall: 0.0600 - auc: 0.9184 - val_loss: 0.1110 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9173
Epoch 18/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1063 - accuracy: 0.9713 - precision: 0.7143 - recall: 0.1000 - auc: 0.9268 - val_loss: 0.1134 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9065
Epoch 19/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1046 - accuracy: 0.9707 - precision: 0.7500 - recall: 0.0600 - auc: 0.9387 - val_loss: 0.1096 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9134
Epoch 20/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1009 - accuracy: 0.9738 - precision: 0.8889 - recall: 0.1600 - auc: 0.9423 - val_loss: 0.1086 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9188
Epoch 21/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1016 - accuracy: 0.9732 - precision: 1.0000 - recall: 0.1200 - auc: 0.9307 - val_loss: 0.1078 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9204
Epoch 22/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1020 - accuracy: 0.9732 - precision: 1.0000 - recall: 0.1200 - auc: 0.9304 - val_loss: 0.1048 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9263
Epoch 23/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0989 - accuracy: 0.9720 - precision: 0.8333 - recall: 0.1000 - auc: 0.9395 - val_loss: 0.1066 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9227
Epoch 24/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1052 - accuracy: 0.9732 - precision: 0.8750 - recall: 0.1400 - auc: 0.9201 - val_loss: 0.1060 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9229
Epoch 25/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1003 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9365 - val_loss: 0.1042 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9296
Epoch 26/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1011 - accuracy: 0.9750 - precision: 1.0000 - recall: 0.1800 - auc: 0.9286 - val_loss: 0.1066 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9184
Epoch 27/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0990 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9316 - val_loss: 0.1069 - val_accuracy: 0.9732 - val_precision: 1.0000 - val_recall: 0.0833 - val_auc: 0.9160
Epoch 28/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1051 - accuracy: 0.9726 - precision: 0.7778 - recall: 0.1400 - auc: 0.9176 - val_loss: 0.1056 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9212
Epoch 29/40
52/52 [==============================] - 0s 3ms/step - loss: 0.0968 - accuracy: 0.9732 - precision: 1.0000 - recall: 0.1200 - auc: 0.9367 - val_loss: 0.1017 - val_accuracy: 0.9683 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9306
Epoch 30/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0988 - accuracy: 0.9732 - precision: 0.8000 - recall: 0.1600 - auc: 0.9268 - val_loss: 0.1049 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9276
Epoch 31/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0998 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9251 - val_loss: 0.1056 - val_accuracy: 0.9732 - val_precision: 1.0000 - val_recall: 0.0833 - val_auc: 0.9140
Epoch 32/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0994 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9371 - val_loss: 0.1050 - val_accuracy: 0.9732 - val_precision: 1.0000 - val_recall: 0.0833 - val_auc: 0.9154
Epoch 33/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1031 - accuracy: 0.9726 - precision: 0.8571 - recall: 0.1200 - auc: 0.9190 - val_loss: 0.1041 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9271
Epoch 34/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0996 - accuracy: 0.9750 - precision: 0.9091 - recall: 0.2000 - auc: 0.9238 - val_loss: 0.1056 - val_accuracy: 0.9732 - val_precision: 0.6667 - val_recall: 0.1667 - val_auc: 0.9152
Epoch 35/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0988 - accuracy: 0.9762 - precision: 1.0000 - recall: 0.2200 - auc: 0.9234 - val_loss: 0.1035 - val_accuracy: 0.9732 - val_precision: 0.6667 - val_recall: 0.1667 - val_auc: 0.9244
Epoch 36/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1026 - accuracy: 0.9744 - precision: 0.8333 - recall: 0.2000 - auc: 0.9163 - val_loss: 0.1064 - val_accuracy: 0.9732 - val_precision: 1.0000 - val_recall: 0.0833 - val_auc: 0.9135
Epoch 37/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1008 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9207 - val_loss: 0.1034 - val_accuracy: 0.9732 - val_precision: 0.6667 - val_recall: 0.1667 - val_auc: 0.9192
Epoch 38/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0995 - accuracy: 0.9750 - precision: 1.0000 - recall: 0.1800 - auc: 0.9278 - val_loss: 0.1036 - val_accuracy: 0.9707 - val_precision: 0.5000 - val_recall: 0.0833 - val_auc: 0.9243
Epoch 39/40
52/52 [==============================] - 0s 2ms/step - loss: 0.0991 - accuracy: 0.9720 - precision: 0.6667 - recall: 0.1600 - auc: 0.9390 - val_loss: 0.1070 - val_accuracy: 0.9732 - val_precision: 0.6000 - val_recall: 0.2500 - val_auc: 0.9164
Epoch 40/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1010 - accuracy: 0.9738 - precision: 0.8182 - recall: 0.1800 - auc: 0.9213 - val_loss: 0.1055 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9220

plot_training_history(history_turner_D1)
df_metrics_D1, val_loss_D1, val_auc_D1, val_precision_D1, val_recall_D1, f1_val_D1 = evaluacion_modelo(history_turner_D1)
print(val_loss_D1, val_auc_D1, val_precision_D1, val_recall_D1, f1_val_D1)
df_metrics_D1

0.1055237203836441 0.9220058917999268 0.0 0.0 0

best_model_tuner_D2, history_turner_D2 = busqueda_hiperparams_tuner(X_train_sub_bal, y_train_bal, X_valid_sub_bal, y_val)

Trial 90 Complete [00h 00m 12s]
val_loss: 1.5843027830123901

Best val_loss So Far: 0.2495328038930893
Total elapsed time: 00h 05m 41s
Mejores hiperparámetros encontrados: {'units_1': 16, 'dropout_1': 0.7, 'units_2': 16, 'dropout_2': 0.4, 'optimizer': 'nadam', 'learning_rate': 0.005155901490534253, 'tuner/epochs': 40, 'tuner/initial_epoch': 14, 'tuner/bracket': 2, 'tuner/round': 2, 'tuner/trial_id': '0067'}
Epoch 1/40
100/100 [==============================] - 2s 6ms/step - loss: 0.8551 - accuracy: 0.6695 - precision: 0.6675 - recall: 0.6755 - auc: 0.7338 - val_loss: 0.6223 - val_accuracy: 0.7976 - val_precision: 0.1099 - val_recall: 0.8333 - val_auc: 0.9070
Epoch 2/40
100/100 [==============================] - 0s 2ms/step - loss: 0.5527 - accuracy: 0.8305 - precision: 0.8202 - recall: 0.8465 - auc: 0.9065 - val_loss: 0.4427 - val_accuracy: 0.8317 - val_precision: 0.1392 - val_recall: 0.9167 - val_auc: 0.9257
Epoch 3/40
100/100 [==============================] - 0s 2ms/step - loss: 0.4236 - accuracy: 0.8862 - precision: 0.8681 - recall: 0.9107 - auc: 0.9417 - val_loss: 0.3409 - val_accuracy: 0.8732 - val_precision: 0.1667 - val_recall: 0.8333 - val_auc: 0.9396
Epoch 4/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3844 - accuracy: 0.8943 - precision: 0.8737 - recall: 0.9220 - auc: 0.9463 - val_loss: 0.3342 - val_accuracy: 0.8683 - val_precision: 0.1379 - val_recall: 0.6667 - val_auc: 0.9225
Epoch 5/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3557 - accuracy: 0.9057 - precision: 0.8750 - recall: 0.9465 - auc: 0.9538 - val_loss: 0.3828 - val_accuracy: 0.8561 - val_precision: 0.1594 - val_recall: 0.9167 - val_auc: 0.9343
Epoch 6/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3449 - accuracy: 0.9088 - precision: 0.8749 - recall: 0.9541 - auc: 0.9537 - val_loss: 0.3323 - val_accuracy: 0.8732 - val_precision: 0.1552 - val_recall: 0.7500 - val_auc: 0.9238
Epoch 7/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3411 - accuracy: 0.9110 - precision: 0.8833 - recall: 0.9472 - auc: 0.9535 - val_loss: 0.3462 - val_accuracy: 0.8683 - val_precision: 0.1613 - val_recall: 0.8333 - val_auc: 0.9317
Epoch 8/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3252 - accuracy: 0.9142 - precision: 0.8826 - recall: 0.9553 - auc: 0.9592 - val_loss: 0.3640 - val_accuracy: 0.8780 - val_precision: 0.1724 - val_recall: 0.8333 - val_auc: 0.9331
Epoch 9/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3279 - accuracy: 0.9132 - precision: 0.8806 - recall: 0.9560 - auc: 0.9544 - val_loss: 0.3241 - val_accuracy: 0.8854 - val_precision: 0.1698 - val_recall: 0.7500 - val_auc: 0.9274
Epoch 10/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3248 - accuracy: 0.9116 - precision: 0.8803 - recall: 0.9528 - auc: 0.9564 - val_loss: 0.3058 - val_accuracy: 0.8902 - val_precision: 0.1887 - val_recall: 0.8333 - val_auc: 0.9316
Epoch 11/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3180 - accuracy: 0.9179 - precision: 0.8839 - recall: 0.9623 - auc: 0.9577 - val_loss: 0.2910 - val_accuracy: 0.8927 - val_precision: 0.1667 - val_recall: 0.6667 - val_auc: 0.9326
Epoch 12/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3246 - accuracy: 0.9164 - precision: 0.8818 - recall: 0.9616 - auc: 0.9553 - val_loss: 0.2963 - val_accuracy: 0.9024 - val_precision: 0.1957 - val_recall: 0.7500 - val_auc: 0.9296
Epoch 13/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.9079 - precision: 0.8733 - recall: 0.9541 - auc: 0.9568 - val_loss: 0.3045 - val_accuracy: 0.8902 - val_precision: 0.1887 - val_recall: 0.8333 - val_auc: 0.9403
Epoch 14/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.9164 - precision: 0.8858 - recall: 0.9560 - auc: 0.9634 - val_loss: 0.3014 - val_accuracy: 0.8976 - val_precision: 0.1591 - val_recall: 0.5833 - val_auc: 0.9240
Epoch 15/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3145 - accuracy: 0.9132 - precision: 0.8838 - recall: 0.9516 - auc: 0.9595 - val_loss: 0.3482 - val_accuracy: 0.8805 - val_precision: 0.1754 - val_recall: 0.8333 - val_auc: 0.9361
Epoch 16/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.9079 - precision: 0.8751 - recall: 0.9516 - auc: 0.9616 - val_loss: 0.3126 - val_accuracy: 0.8927 - val_precision: 0.1923 - val_recall: 0.8333 - val_auc: 0.9250
Epoch 17/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3091 - accuracy: 0.9145 - precision: 0.8809 - recall: 0.9585 - auc: 0.9609 - val_loss: 0.2936 - val_accuracy: 0.8976 - val_precision: 0.1591 - val_recall: 0.5833 - val_auc: 0.9307
Epoch 18/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3127 - accuracy: 0.9170 - precision: 0.8841 - recall: 0.9597 - auc: 0.9603 - val_loss: 0.3054 - val_accuracy: 0.8976 - val_precision: 0.1875 - val_recall: 0.7500 - val_auc: 0.9337
Epoch 19/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3131 - accuracy: 0.9160 - precision: 0.8795 - recall: 0.9642 - auc: 0.9595 - val_loss: 0.3105 - val_accuracy: 0.8976 - val_precision: 0.2000 - val_recall: 0.8333 - val_auc: 0.9322
Epoch 20/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3148 - accuracy: 0.9186 - precision: 0.8840 - recall: 0.9635 - auc: 0.9584 - val_loss: 0.3477 - val_accuracy: 0.8732 - val_precision: 0.1667 - val_recall: 0.8333 - val_auc: 0.9385
Epoch 21/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3080 - accuracy: 0.9176 - precision: 0.8856 - recall: 0.9591 - auc: 0.9610 - val_loss: 0.3451 - val_accuracy: 0.8829 - val_precision: 0.1667 - val_recall: 0.7500 - val_auc: 0.9359
Epoch 22/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3063 - accuracy: 0.9145 - precision: 0.8845 - recall: 0.9535 - auc: 0.9626 - val_loss: 0.3527 - val_accuracy: 0.8780 - val_precision: 0.1607 - val_recall: 0.7500 - val_auc: 0.9203
Epoch 23/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3058 - accuracy: 0.9201 - precision: 0.8848 - recall: 0.9660 - auc: 0.9609 - val_loss: 0.3170 - val_accuracy: 0.8756 - val_precision: 0.1579 - val_recall: 0.7500 - val_auc: 0.9303
Epoch 24/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3092 - accuracy: 0.9126 - precision: 0.8757 - recall: 0.9616 - auc: 0.9599 - val_loss: 0.3065 - val_accuracy: 0.8878 - val_precision: 0.1600 - val_recall: 0.6667 - val_auc: 0.9277
Epoch 25/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3048 - accuracy: 0.9167 - precision: 0.8858 - recall: 0.9566 - auc: 0.9608 - val_loss: 0.3140 - val_accuracy: 0.8902 - val_precision: 0.1887 - val_recall: 0.8333 - val_auc: 0.9368
Epoch 26/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3130 - accuracy: 0.9129 - precision: 0.8784 - recall: 0.9585 - auc: 0.9600 - val_loss: 0.3026 - val_accuracy: 0.8927 - val_precision: 0.1923 - val_recall: 0.8333 - val_auc: 0.9295
Epoch 27/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2990 - accuracy: 0.9198 - precision: 0.8879 - recall: 0.9610 - auc: 0.9649 - val_loss: 0.3474 - val_accuracy: 0.8854 - val_precision: 0.1818 - val_recall: 0.8333 - val_auc: 0.9351
Epoch 28/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3049 - accuracy: 0.9145 - precision: 0.8827 - recall: 0.9560 - auc: 0.9607 - val_loss: 0.3584 - val_accuracy: 0.8805 - val_precision: 0.1754 - val_recall: 0.8333 - val_auc: 0.9274
Epoch 29/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3228 - accuracy: 0.9107 - precision: 0.8770 - recall: 0.9553 - auc: 0.9592 - val_loss: 0.3416 - val_accuracy: 0.8854 - val_precision: 0.1569 - val_recall: 0.6667 - val_auc: 0.9187
Epoch 30/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3192 - accuracy: 0.9132 - precision: 0.8802 - recall: 0.9566 - auc: 0.9584 - val_loss: 0.3316 - val_accuracy: 0.8756 - val_precision: 0.1579 - val_recall: 0.7500 - val_auc: 0.9238
Epoch 31/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3125 - accuracy: 0.9069 - precision: 0.8766 - recall: 0.9472 - auc: 0.9609 - val_loss: 0.3300 - val_accuracy: 0.8780 - val_precision: 0.1607 - val_recall: 0.7500 - val_auc: 0.9268
Epoch 32/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3081 - accuracy: 0.9164 - precision: 0.8805 - recall: 0.9635 - auc: 0.9604 - val_loss: 0.3079 - val_accuracy: 0.8829 - val_precision: 0.1667 - val_recall: 0.7500 - val_auc: 0.9311
Epoch 33/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3131 - accuracy: 0.9132 - precision: 0.8754 - recall: 0.9635 - auc: 0.9582 - val_loss: 0.2897 - val_accuracy: 0.8951 - val_precision: 0.1961 - val_recall: 0.8333 - val_auc: 0.9347
Epoch 34/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3085 - accuracy: 0.9160 - precision: 0.8813 - recall: 0.9616 - auc: 0.9587 - val_loss: 0.3108 - val_accuracy: 0.8878 - val_precision: 0.1852 - val_recall: 0.8333 - val_auc: 0.9172
Epoch 35/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3012 - accuracy: 0.9135 - precision: 0.8794 - recall: 0.9585 - auc: 0.9632 - val_loss: 0.3183 - val_accuracy: 0.8927 - val_precision: 0.1800 - val_recall: 0.7500 - val_auc: 0.9298
Epoch 36/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2903 - accuracy: 0.9233 - precision: 0.8904 - recall: 0.9654 - auc: 0.9654 - val_loss: 0.2959 - val_accuracy: 0.9000 - val_precision: 0.1915 - val_recall: 0.7500 - val_auc: 0.9326
Epoch 37/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3038 - accuracy: 0.9148 - precision: 0.8788 - recall: 0.9623 - auc: 0.9608 - val_loss: 0.3284 - val_accuracy: 0.8878 - val_precision: 0.1852 - val_recall: 0.8333 - val_auc: 0.9306
Epoch 38/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3066 - accuracy: 0.9157 - precision: 0.8799 - recall: 0.9629 - auc: 0.9612 - val_loss: 0.2963 - val_accuracy: 0.8976 - val_precision: 0.1875 - val_recall: 0.7500 - val_auc: 0.9285
Epoch 39/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3073 - accuracy: 0.9211 - precision: 0.8917 - recall: 0.9585 - auc: 0.9581 - val_loss: 0.3046 - val_accuracy: 0.8805 - val_precision: 0.1509 - val_recall: 0.6667 - val_auc: 0.9316
Epoch 40/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2990 - accuracy: 0.9160 - precision: 0.8844 - recall: 0.9572 - auc: 0.9630 - val_loss: 0.3543 - val_accuracy: 0.8780 - val_precision: 0.1607 - val_recall: 0.7500 - val_auc: 0.9326

plot_training_history(history_turner_D2)
df_metrics_D2, val_loss_D2, val_auc_D2, val_precision_D2, val_recall_D2, f1_val_D2 = evaluacion_modelo(history_turner_D2)
print(val_loss_D2, val_auc_D2, val_precision_D2, val_recall_D2, f1_val_D2)
df_metrics_D2

0.3543453812599182 0.9325795769691467 0.1607142835855484 0.75 0.26470587946551893

best_model_tuner_D3, history_turner_D3 = busqueda_hiperparams_tuner(X_train_pca, y_train, X_val_pca, y_val)

Trial 90 Complete [00h 00m 09s]
val_loss: 0.114965058863163

Best val_loss So Far: 0.10507414489984512
Total elapsed time: 00h 04m 38s
Mejores hiperparámetros encontrados: {'units_1': 32, 'dropout_1': 0.30000000000000004, 'units_2': 16, 'dropout_2': 0.2, 'optimizer': 'adam', 'learning_rate': 0.0028325744723759023, 'tuner/epochs': 40, 'tuner/initial_epoch': 14, 'tuner/bracket': 1, 'tuner/round': 1, 'tuner/trial_id': '0075'}
Epoch 1/40
52/52 [==============================] - 1s 11ms/step - loss: 0.9150 - accuracy: 0.7835 - precision: 0.0336 - recall: 0.2200 - auc: 0.5304 - val_loss: 0.5405 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.5581
Epoch 2/40
52/52 [==============================] - 0s 2ms/step - loss: 0.4851 - accuracy: 0.9701 - precision: 0.6667 - recall: 0.0400 - auc: 0.7050 - val_loss: 0.4014 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7084
Epoch 3/40
52/52 [==============================] - 0s 2ms/step - loss: 0.3712 - accuracy: 0.9695 - precision: 0.5000 - recall: 0.0400 - auc: 0.7839 - val_loss: 0.3196 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8328
Epoch 4/40
52/52 [==============================] - 0s 2ms/step - loss: 0.2935 - accuracy: 0.9701 - precision: 1.0000 - recall: 0.0200 - auc: 0.8675 - val_loss: 0.2665 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8684
Epoch 5/40
52/52 [==============================] - 0s 2ms/step - loss: 0.2484 - accuracy: 0.9707 - precision: 0.6667 - recall: 0.0800 - auc: 0.8904 - val_loss: 0.2297 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8775
Epoch 6/40
52/52 [==============================] - 0s 2ms/step - loss: 0.2070 - accuracy: 0.9720 - precision: 0.8333 - recall: 0.1000 - auc: 0.9219 - val_loss: 0.1985 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9073
Epoch 7/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1848 - accuracy: 0.9720 - precision: 0.8333 - recall: 0.1000 - auc: 0.9182 - val_loss: 0.1759 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9151
Epoch 8/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1662 - accuracy: 0.9726 - precision: 1.0000 - recall: 0.1000 - auc: 0.9158 - val_loss: 0.1610 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9199
Epoch 9/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1505 - accuracy: 0.9726 - precision: 1.0000 - recall: 0.1000 - auc: 0.9315 - val_loss: 0.1512 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9132
Epoch 10/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1432 - accuracy: 0.9720 - precision: 1.0000 - recall: 0.0800 - auc: 0.9213 - val_loss: 0.1425 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9193
Epoch 11/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1312 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9357 - val_loss: 0.1368 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9181
Epoch 12/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1257 - accuracy: 0.9726 - precision: 1.0000 - recall: 0.1000 - auc: 0.9365 - val_loss: 0.1306 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9155
Epoch 13/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1245 - accuracy: 0.9732 - precision: 0.8750 - recall: 0.1400 - auc: 0.9245 - val_loss: 0.1281 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9112
Epoch 14/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1248 - accuracy: 0.9726 - precision: 1.0000 - recall: 0.1000 - auc: 0.9165 - val_loss: 0.1227 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9264
Epoch 15/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1155 - accuracy: 0.9732 - precision: 1.0000 - recall: 0.1200 - auc: 0.9312 - val_loss: 0.1197 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9233
Epoch 16/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1125 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9294 - val_loss: 0.1185 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9186
Epoch 17/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1139 - accuracy: 0.9744 - precision: 0.9000 - recall: 0.1800 - auc: 0.9184 - val_loss: 0.1151 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9234
Epoch 18/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1143 - accuracy: 0.9738 - precision: 0.8182 - recall: 0.1800 - auc: 0.9248 - val_loss: 0.1185 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9140
Epoch 19/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1081 - accuracy: 0.9750 - precision: 1.0000 - recall: 0.1800 - auc: 0.9351 - val_loss: 0.1129 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8991
Epoch 20/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1069 - accuracy: 0.9744 - precision: 0.9000 - recall: 0.1800 - auc: 0.9198 - val_loss: 0.1108 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9255
Epoch 21/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1019 - accuracy: 0.9750 - precision: 0.9091 - recall: 0.2000 - auc: 0.9486 - val_loss: 0.1110 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9236
Epoch 22/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1043 - accuracy: 0.9744 - precision: 0.9000 - recall: 0.1800 - auc: 0.9341 - val_loss: 0.1098 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9277
Epoch 23/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1063 - accuracy: 0.9732 - precision: 1.0000 - recall: 0.1200 - auc: 0.9228 - val_loss: 0.1120 - val_accuracy: 0.9683 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9112
Epoch 24/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1104 - accuracy: 0.9726 - precision: 0.8571 - recall: 0.1200 - auc: 0.9128 - val_loss: 0.1098 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9210
Epoch 25/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1055 - accuracy: 0.9756 - precision: 1.0000 - recall: 0.2000 - auc: 0.9265 - val_loss: 0.1081 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9320
Epoch 26/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1060 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9256 - val_loss: 0.1110 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9206
Epoch 27/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1045 - accuracy: 0.9756 - precision: 1.0000 - recall: 0.2000 - auc: 0.9251 - val_loss: 0.1084 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9227
Epoch 28/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1061 - accuracy: 0.9744 - precision: 0.9000 - recall: 0.1800 - auc: 0.9086 - val_loss: 0.1086 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9154
Epoch 29/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1003 - accuracy: 0.9750 - precision: 0.9091 - recall: 0.2000 - auc: 0.9339 - val_loss: 0.1084 - val_accuracy: 0.9707 - val_precision: 0.5000 - val_recall: 0.0833 - val_auc: 0.8960
Epoch 30/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1049 - accuracy: 0.9762 - precision: 1.0000 - recall: 0.2200 - auc: 0.9093 - val_loss: 0.1132 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9091
Epoch 31/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1032 - accuracy: 0.9726 - precision: 0.8571 - recall: 0.1200 - auc: 0.9351 - val_loss: 0.1098 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8922
Epoch 32/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1032 - accuracy: 0.9738 - precision: 0.8889 - recall: 0.1600 - auc: 0.9208 - val_loss: 0.1090 - val_accuracy: 0.9683 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8923
Epoch 33/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1033 - accuracy: 0.9756 - precision: 0.9167 - recall: 0.2200 - auc: 0.9256 - val_loss: 0.1101 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9142
Epoch 34/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1037 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9216 - val_loss: 0.1086 - val_accuracy: 0.9732 - val_precision: 0.6667 - val_recall: 0.1667 - val_auc: 0.9135
Epoch 35/40
52/52 [==============================] - 0s 3ms/step - loss: 0.1077 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9072 - val_loss: 0.1102 - val_accuracy: 0.9683 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9098
Epoch 36/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1060 - accuracy: 0.9744 - precision: 1.0000 - recall: 0.1600 - auc: 0.9240 - val_loss: 0.1123 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9105
Epoch 37/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1062 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9145 - val_loss: 0.1082 - val_accuracy: 0.9707 - val_precision: 0.5000 - val_recall: 0.0833 - val_auc: 0.9169
Epoch 38/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1011 - accuracy: 0.9756 - precision: 1.0000 - recall: 0.2000 - auc: 0.9356 - val_loss: 0.1074 - val_accuracy: 0.9683 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9271
Epoch 39/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1052 - accuracy: 0.9738 - precision: 1.0000 - recall: 0.1400 - auc: 0.9281 - val_loss: 0.1121 - val_accuracy: 0.9707 - val_precision: 0.5000 - val_recall: 0.0833 - val_auc: 0.8883
Epoch 40/40
52/52 [==============================] - 0s 2ms/step - loss: 0.1031 - accuracy: 0.9756 - precision: 0.9167 - recall: 0.2200 - auc: 0.9245 - val_loss: 0.1073 - val_accuracy: 0.9707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.9169

plot_training_history(history_turner_D3)
df_metrics_D3, val_loss_D3, val_auc_D3, val_precision_D3, val_recall_D3, f1_val_D3 = evaluacion_modelo(history_turner_D3)
print(val_loss_D3, val_auc_D3, val_precision_D3, val_recall_D3, f1_val_D3)
df_metrics_D3

0.10726682841777802 0.9168760776519775 0.0 0.0 0

best_model_tuner_D4, history_turner_D4 = busqueda_hiperparams_tuner(X_train_pca_bal, y_train_bal, X_val_pca, y_val)

Trial 90 Complete [00h 00m 10s]
val_loss: 0.4031321406364441

Best val_loss So Far: 0.2514355480670929
Total elapsed time: 00h 05m 39s
Mejores hiperparámetros encontrados: {'units_1': 16, 'dropout_1': 0.6000000000000001, 'units_2': 32, 'dropout_2': 0.4, 'optimizer': 'nadam', 'learning_rate': 0.010731058974110062, 'tuner/epochs': 5, 'tuner/initial_epoch': 2, 'tuner/bracket': 3, 'tuner/round': 1, 'tuner/trial_id': '0022'}
Epoch 1/40

100/100 [==============================] - 2s 6ms/step - loss: 0.6660 - accuracy: 0.7607 - precision: 0.7589 - recall: 0.7642 - auc: 0.8497 - val_loss: 0.4480 - val_accuracy: 0.8561 - val_precision: 0.1594 - val_recall: 0.9167 - val_auc: 0.9261
Epoch 2/40
100/100 [==============================] - 0s 2ms/step - loss: 0.4209 - accuracy: 0.8858 - precision: 0.8689 - recall: 0.9088 - auc: 0.9424 - val_loss: 0.4048 - val_accuracy: 0.8512 - val_precision: 0.1549 - val_recall: 0.9167 - val_auc: 0.9214
Epoch 3/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3547 - accuracy: 0.9097 - precision: 0.8759 - recall: 0.9547 - auc: 0.9546 - val_loss: 0.2759 - val_accuracy: 0.9000 - val_precision: 0.1778 - val_recall: 0.6667 - val_auc: 0.9367
Epoch 4/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.9145 - precision: 0.8792 - recall: 0.9610 - auc: 0.9597 - val_loss: 0.3303 - val_accuracy: 0.8951 - val_precision: 0.1961 - val_recall: 0.8333 - val_auc: 0.9266
Epoch 5/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3428 - accuracy: 0.9148 - precision: 0.8741 - recall: 0.9692 - auc: 0.9600 - val_loss: 0.3181 - val_accuracy: 0.8878 - val_precision: 0.1852 - val_recall: 0.8333 - val_auc: 0.9410
Epoch 6/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3280 - accuracy: 0.9195 - precision: 0.8807 - recall: 0.9704 - auc: 0.9652 - val_loss: 0.2850 - val_accuracy: 0.9146 - val_precision: 0.1892 - val_recall: 0.5833 - val_auc: 0.8973
Epoch 7/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3193 - accuracy: 0.9173 - precision: 0.8811 - recall: 0.9648 - auc: 0.9652 - val_loss: 0.2770 - val_accuracy: 0.9146 - val_precision: 0.2195 - val_recall: 0.7500 - val_auc: 0.9088
Epoch 8/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3226 - accuracy: 0.9189 - precision: 0.8828 - recall: 0.9660 - auc: 0.9643 - val_loss: 0.3034 - val_accuracy: 0.9171 - val_precision: 0.2500 - val_recall: 0.9167 - val_auc: 0.9468
Epoch 9/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3444 - accuracy: 0.9138 - precision: 0.8739 - recall: 0.9673 - auc: 0.9614 - val_loss: 0.2990 - val_accuracy: 0.9195 - val_precision: 0.2308 - val_recall: 0.7500 - val_auc: 0.9112
Epoch 10/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3435 - accuracy: 0.9186 - precision: 0.8801 - recall: 0.9692 - auc: 0.9629 - val_loss: 0.3172 - val_accuracy: 0.9049 - val_precision: 0.1707 - val_recall: 0.5833 - val_auc: 0.8867
Epoch 11/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3148 - accuracy: 0.9258 - precision: 0.8860 - recall: 0.9774 - auc: 0.9685 - val_loss: 0.2873 - val_accuracy: 0.9220 - val_precision: 0.2222 - val_recall: 0.6667 - val_auc: 0.9044
Epoch 12/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3106 - accuracy: 0.9270 - precision: 0.8898 - recall: 0.9748 - auc: 0.9699 - val_loss: 0.2721 - val_accuracy: 0.9341 - val_precision: 0.2222 - val_recall: 0.5000 - val_auc: 0.8874
Epoch 13/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3631 - accuracy: 0.9192 - precision: 0.8806 - recall: 0.9698 - auc: 0.9614 - val_loss: 0.2986 - val_accuracy: 0.9122 - val_precision: 0.1667 - val_recall: 0.5000 - val_auc: 0.9308
Epoch 14/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3204 - accuracy: 0.9214 - precision: 0.8802 - recall: 0.9755 - auc: 0.9674 - val_loss: 0.2818 - val_accuracy: 0.9195 - val_precision: 0.1818 - val_recall: 0.5000 - val_auc: 0.8862
Epoch 15/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3166 - accuracy: 0.9252 - precision: 0.8930 - recall: 0.9660 - auc: 0.9692 - val_loss: 0.3500 - val_accuracy: 0.8902 - val_precision: 0.1633 - val_recall: 0.6667 - val_auc: 0.8980
Epoch 16/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3135 - accuracy: 0.9277 - precision: 0.8940 - recall: 0.9704 - auc: 0.9710 - val_loss: 0.2984 - val_accuracy: 0.9098 - val_precision: 0.1622 - val_recall: 0.5000 - val_auc: 0.8900
Epoch 17/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3203 - accuracy: 0.9261 - precision: 0.8937 - recall: 0.9673 - auc: 0.9718 - val_loss: 0.3393 - val_accuracy: 0.9220 - val_precision: 0.1875 - val_recall: 0.5000 - val_auc: 0.8875
Epoch 18/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3306 - accuracy: 0.9239 - precision: 0.8891 - recall: 0.9686 - auc: 0.9694 - val_loss: 0.2974 - val_accuracy: 0.9122 - val_precision: 0.1842 - val_recall: 0.5833 - val_auc: 0.8965
Epoch 19/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2936 - accuracy: 0.9286 - precision: 0.8928 - recall: 0.9742 - auc: 0.9731 - val_loss: 0.2681 - val_accuracy: 0.9268 - val_precision: 0.2353 - val_recall: 0.6667 - val_auc: 0.9093
Epoch 20/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2965 - accuracy: 0.9299 - precision: 0.8921 - recall: 0.9780 - auc: 0.9725 - val_loss: 0.3285 - val_accuracy: 0.9049 - val_precision: 0.2000 - val_recall: 0.7500 - val_auc: 0.9070
Epoch 21/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2973 - accuracy: 0.9308 - precision: 0.8946 - recall: 0.9767 - auc: 0.9710 - val_loss: 0.2981 - val_accuracy: 0.9171 - val_precision: 0.2250 - val_recall: 0.7500 - val_auc: 0.9130
Epoch 22/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3108 - accuracy: 0.9173 - precision: 0.8807 - recall: 0.9654 - auc: 0.9684 - val_loss: 0.3077 - val_accuracy: 0.9146 - val_precision: 0.2051 - val_recall: 0.6667 - val_auc: 0.8920
Epoch 23/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3028 - accuracy: 0.9289 - precision: 0.8956 - recall: 0.9711 - auc: 0.9695 - val_loss: 0.3025 - val_accuracy: 0.9146 - val_precision: 0.1892 - val_recall: 0.5833 - val_auc: 0.8999
Epoch 24/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3165 - accuracy: 0.9201 - precision: 0.8848 - recall: 0.9660 - auc: 0.9699 - val_loss: 0.2869 - val_accuracy: 0.9220 - val_precision: 0.2059 - val_recall: 0.5833 - val_auc: 0.9218
Epoch 25/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3069 - accuracy: 0.9299 - precision: 0.8962 - recall: 0.9723 - auc: 0.9715 - val_loss: 0.3252 - val_accuracy: 0.9073 - val_precision: 0.1750 - val_recall: 0.5833 - val_auc: 0.8999
Epoch 26/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2909 - accuracy: 0.9305 - precision: 0.8973 - recall: 0.9723 - auc: 0.9738 - val_loss: 0.2898 - val_accuracy: 0.9293 - val_precision: 0.2571 - val_recall: 0.7500 - val_auc: 0.9053
Epoch 27/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3037 - accuracy: 0.9274 - precision: 0.8953 - recall: 0.9679 - auc: 0.9735 - val_loss: 0.3172 - val_accuracy: 0.9073 - val_precision: 0.2174 - val_recall: 0.8333 - val_auc: 0.9428
Epoch 28/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2973 - accuracy: 0.9277 - precision: 0.8926 - recall: 0.9723 - auc: 0.9707 - val_loss: 0.3026 - val_accuracy: 0.9171 - val_precision: 0.2105 - val_recall: 0.6667 - val_auc: 0.8974
Epoch 29/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3031 - accuracy: 0.9245 - precision: 0.8879 - recall: 0.9717 - auc: 0.9713 - val_loss: 0.2714 - val_accuracy: 0.9317 - val_precision: 0.2500 - val_recall: 0.6667 - val_auc: 0.9099
Epoch 30/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2983 - accuracy: 0.9223 - precision: 0.8884 - recall: 0.9660 - auc: 0.9715 - val_loss: 0.3075 - val_accuracy: 0.9024 - val_precision: 0.2083 - val_recall: 0.8333 - val_auc: 0.9067
Epoch 31/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3023 - accuracy: 0.9242 - precision: 0.8883 - recall: 0.9704 - auc: 0.9690 - val_loss: 0.3084 - val_accuracy: 0.9024 - val_precision: 0.1667 - val_recall: 0.5833 - val_auc: 0.8924
Epoch 32/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3292 - accuracy: 0.9151 - precision: 0.8793 - recall: 0.9623 - auc: 0.9641 - val_loss: 0.3115 - val_accuracy: 0.9195 - val_precision: 0.2162 - val_recall: 0.6667 - val_auc: 0.8901
Epoch 33/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3652 - accuracy: 0.9176 - precision: 0.8794 - recall: 0.9679 - auc: 0.9610 - val_loss: 0.2908 - val_accuracy: 0.9317 - val_precision: 0.2333 - val_recall: 0.5833 - val_auc: 0.8897
Epoch 34/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3325 - accuracy: 0.9236 - precision: 0.8882 - recall: 0.9692 - auc: 0.9663 - val_loss: 0.3163 - val_accuracy: 0.9049 - val_precision: 0.1538 - val_recall: 0.5000 - val_auc: 0.8733
Epoch 35/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2938 - accuracy: 0.9343 - precision: 0.8980 - recall: 0.9799 - auc: 0.9742 - val_loss: 0.2718 - val_accuracy: 0.9463 - val_precision: 0.2727 - val_recall: 0.5000 - val_auc: 0.9010
Epoch 36/40
100/100 [==============================] - 0s 2ms/step - loss: 0.2923 - accuracy: 0.9270 - precision: 0.8916 - recall: 0.9723 - auc: 0.9722 - val_loss: 0.3382 - val_accuracy: 0.9171 - val_precision: 0.2250 - val_recall: 0.7500 - val_auc: 0.8891
Epoch 37/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3407 - accuracy: 0.9164 - precision: 0.8753 - recall: 0.9711 - auc: 0.9652 - val_loss: 0.3435 - val_accuracy: 0.8976 - val_precision: 0.2000 - val_recall: 0.8333 - val_auc: 0.8910
Epoch 38/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3242 - accuracy: 0.9286 - precision: 0.8937 - recall: 0.9730 - auc: 0.9675 - val_loss: 0.3603 - val_accuracy: 0.9024 - val_precision: 0.1957 - val_recall: 0.7500 - val_auc: 0.8916
Epoch 39/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3110 - accuracy: 0.9264 - precision: 0.8883 - recall: 0.9755 - auc: 0.9658 - val_loss: 0.3704 - val_accuracy: 0.8805 - val_precision: 0.1636 - val_recall: 0.7500 - val_auc: 0.8834
Epoch 40/40
100/100 [==============================] - 0s 2ms/step - loss: 0.3141 - accuracy: 0.9248 - precision: 0.8898 - recall: 0.9698 - auc: 0.9661 - val_loss: 0.3750 - val_accuracy: 0.9049 - val_precision: 0.2000 - val_recall: 0.7500 - val_auc: 0.8901

plot_training_history(history_turner_D4)
df_metrics_D4, val_loss_D4, val_auc_D4, val_precision_D4, val_recall_D4, f1_val_D4 = evaluacion_modelo(history_turner_D4)
print(val_loss_D4, val_auc_D4, val_precision_D4, val_recall_D4, f1_val_D4)
df_metrics_D4

0.37500354647636414 0.8900754451751709 0.20000000298023224 0.75 0.31578947739918145

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Datos de las métricas
datos = [
    0.2691952586174011, 0.685092031955719, 0.375, 0.25, 0.3, #Base
    0.10754568874835968, 0.9216917157173157, 0.5, 0.0833333358168602, 0.1428571465064068, #Base+reg
    0.11583803594112396, 0.9125838279724121, 0.0, 0.0, 0, #Manual
    0.11519674211740494, 0.9068257808685303, 0.0, 0.0, 0, #RS
    0.11414438486099243, 0.915724515914917, 0.0, 0.0, 0, #GS
    0.1055237203836441, 0.9220058917999268, 0.0, 0.0, 0, #Turner_D1
    0.3543453812599182, 0.9325795769691467, 0.1607142835855484, 0.75, 0.2647058794655189, #Turner_D2
    0.10726682841777802, 0.9168760776519775, 0.0, 0.0, 0, #Turner_D3
    0.37500354647636414, 0.8900754451751709, 0.20000000298023224, 0.75, 0.31578947739918145 #Turner_D4
]

# Lista de nombres de los modelos
nombres_modelos = [
    'Base_D1', 'Base+Reg_D1', 'Manual_D1', 
    'RS_D1', 'GS_D1', 'Turner_D1',
    'Turner_D2', 'Turner_D3', 'Turner_D4'
]

# Crear el DataFrame
df_metrics_base = pd.DataFrame(
    [datos[i:i+5] for i in range(0, len(datos), 5)], 
    columns=['val_loss', 'val_auc', 'val_precision', 'val_recall', 'f1_val']
)

# Asignar los nombres personalizados
df_metrics_base['Modelo'] = nombres_modelos[:len(df_metrics_base)]

# Reordenar columnas
df_metrics_base = df_metrics_base[['Modelo', 'val_loss', 'val_auc', 'val_precision', 'val_recall', 'f1_val']]

print(df_metrics_base)

# Graficar
sns.set(style="whitegrid")
fig, axs = plt.subplots(2, 3, figsize=(18, 10))
fig.suptitle('Comparación de Métricas de Modelos', fontsize=20)

# Gráfica val_loss
sns.barplot(x='Modelo', y='val_loss', data=df_metrics_base, ax=axs[0,0], palette="Blues_r")
axs[0,0].set_title('Validation Loss')
axs[0,0].tick_params(axis='x', rotation=45)

# Gráfica val_auc
sns.barplot(x='Modelo', y='val_auc', data=df_metrics_base, ax=axs[0,1], palette="Greens_r")
axs[0,1].set_title('Validation AUC')
axs[0,1].tick_params(axis='x', rotation=45)

# Gráfica val_precision
sns.barplot(x='Modelo', y='val_precision', data=df_metrics_base, ax=axs[0,2], palette="Oranges_r")
axs[0,2].set_title('Validation Precision')
axs[0,2].tick_params(axis='x', rotation=45)

# Gráfica val_recall
sns.barplot(x='Modelo', y='val_recall', data=df_metrics_base, ax=axs[1,0], palette="Purples_r")
axs[1,0].set_title('Validation Recall')
axs[1,0].tick_params(axis='x', rotation=45)

# Gráfica f1_val
sns.barplot(x='Modelo', y='f1_val', data=df_metrics_base, ax=axs[1,1], palette="Reds_r")
axs[1,1].set_title('Validation F1-Score')
axs[1,1].tick_params(axis='x', rotation=45)

# Eliminar la gráfica vacía
fig.delaxes(axs[1,2])

plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()

        Modelo  val_loss   val_auc  val_precision  val_recall    f1_val
0      Base_D1  0.269195  0.685092       0.375000    0.250000  0.300000
1  Base+Reg_D1  0.107546  0.921692       0.500000    0.083333  0.142857
2    Manual_D1  0.115838  0.912584       0.000000    0.000000  0.000000
3        RS_D1  0.115197  0.906826       0.000000    0.000000  0.000000
4        GS_D1  0.114144  0.915725       0.000000    0.000000  0.000000
5    Turner_D1  0.105524  0.922006       0.000000    0.000000  0.000000
6    Turner_D2  0.354345  0.932580       0.160714    0.750000  0.264706
7    Turner_D3  0.107267  0.916876       0.000000    0.000000  0.000000
8    Turner_D4  0.375004  0.890075       0.200000    0.750000  0.315789

	ID	Bankruptcy	Realized.Sales.Gross.Profit.Growth.Rate	Operating.Expense.Rate	Operating.Profit.Per.Share..Yuan...	Gross.Profit.to.Sales	Tax.rate..A.	Current.Liability.to.Liability	Continuous.Net.Profit.Growth.Rate	Long.term.Liability.to.Current.Assets	...	Per.Share.Net.profit.before.tax..Yuan...	Continuous.interest.rate..after.tax.	Liability.Assets.Flag	Working.Capital.to.Total.Assets	Degree.of.Financial.Leverage..DFL.	Operating.Gross.Margin	Contingent.liabilities.Net.worth	Operating.Profit.Growth.Rate	Cash.Flow.to.Liability	Quick.Assets.Total.Assets
0	1	0	0.087661	1.900349e-04	0.131094	0.583345	0.000000	0.815892	0.395719	0.089276	...	0.223720	0.801737	0	0.839112	0.036551	0.591350	0.007401	0.913175	0.489822	0.138714
1	2	0	0.021892	5.371576e-04	0.086167	0.695478	0.209649	0.768636	0.163505	0.000000	...	0.324763	0.791203	0	0.908450	0.126752	0.766066	0.020514	0.849241	0.550247	0.704211
2	3	0	0.102249	2.178329e-04	0.157755	0.766934	0.272061	0.643316	0.181571	0.010180	...	0.360892	0.803126	0	0.865943	0.018152	0.687211	0.004363	0.865814	0.447168	0.398086
3	4	0	0.029972	4.915756e+10	0.249279	0.611580	0.288357	0.752782	0.409196	0.018603	...	0.152445	0.824732	0	0.806254	0.051284	0.715973	0.014162	0.843357	0.485163	0.617386
4	6	0	0.021164	1.206001e-04	0.239817	0.614250	0.000000	0.559498	0.286989	0.148687	...	0.217282	0.787010	0	0.769843	0.012872	0.704043	0.003050	0.884905	0.422001	0.187121
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2045	2545	0	0.067900	7.201851e+05	0.334972	0.564459	0.166696	0.889719	0.277489	0.005494	...	0.177566	0.793529	0	0.860448	0.020242	0.720086	0.006337	0.867445	0.443956	0.699702
2046	2546	0	0.067735	2.380508e-05	0.193017	0.736555	0.089094	0.738665	0.348624	0.007731	...	0.383780	0.830696	0	0.842840	0.159408	0.603963	0.025980	0.824779	0.550102	0.551980
2047	2547	0	0.068447	8.325822e-05	0.109198	0.734305	0.000000	0.534512	0.404424	0.029523	...	0.265202	0.757833	0	0.759694	0.026616	0.741242	0.007079	0.876836	0.666202	0.311649
2048	2548	0	0.017600	9.098720e-03	0.279550	0.757877	0.492404	0.806436	0.175130	0.014910	...	0.380793	0.799704	0	0.791138	0.136190	0.656832	0.003518	0.900142	0.471446	0.401341
2049	2550	0	0.136419	4.421828e+07	0.200310	0.561358	0.126222	0.641650	0.283364	0.025015	...	0.308645	0.761051	0	0.735188	0.042556	0.591996	0.005567	0.906222	0.485466	0.303517

	ID	Realized.Sales.Gross.Profit.Growth.Rate	Operating.Expense.Rate	Operating.Profit.Per.Share..Yuan...	Gross.Profit.to.Sales	Tax.rate..A.	Current.Liability.to.Liability	Continuous.Net.Profit.Growth.Rate	Long.term.Liability.to.Current.Assets	Total.assets.to.GNP.price	...	Per.Share.Net.profit.before.tax..Yuan...	Continuous.interest.rate..after.tax.	Working.Capital.to.Total.Assets	Degree.of.Financial.Leverage..DFL.	Operating.Gross.Margin	Contingent.liabilities.Net.worth	Operating.Profit.Growth.Rate	Cash.Flow.to.Liability	Quick.Assets.Total.Assets
0	5	0.055077	1.064430e-02	0.131220	0.657541	0.118122	0.771297	0.366505	0.018675	0.001246	...	0.197730	0.786381	0.828263	0.024649	0.645659	0.052743	0.913336	0.557402	0.360819
1	14	0.020286	6.654634e+07	0.247104	0.746134	0.000000	0.697656	0.313514	0.126093	0.006813	...	0.254563	0.850153	0.684968	0.075233	0.723486	0.003821	0.851428	0.584835	0.058199
2	16	0.040610	8.415991e+08	0.164832	0.718794	0.130647	0.398228	0.239002	0.122627	0.002547	...	0.186794	0.808544	0.888590	0.078625	0.737439	0.070270	0.832491	0.409327	0.449420
3	26	0.067641	1.344961e-04	0.153400	0.741510	0.000000	0.842550	0.348637	0.007500	0.000219	...	0.401842	0.830902	0.800685	0.158704	0.610681	0.012037	0.824596	0.561291	0.449031
4	28	0.017546	6.125562e-03	0.208468	0.743188	0.317456	0.387746	0.175101	0.039756	0.008112	...	0.329068	0.799609	0.824782	0.136387	0.637626	0.003518	0.900079	0.477007	0.395706

Variable original	Traducción al español	Descripción	Tipo de dato
ID	ID	Identificador único de la observación	int
Bankruptcy	Quiebra	Variable objetivo (0 = no quebró, 1 = quebró)	int
Realized.Sales.Gross.Profit.Growth.Rate	Tasa de crecimiento del beneficio bruto sobre ventas realizadas	Mide el crecimiento del beneficio bruto	float
Operating.Expense.Rate	Tasa de gastos operativos	Gastos operativos / Ingresos	float
Operating.Profit.Per.Share..Yuan...	Beneficio operativo por acción (en Yuanes)	Utilidad operativa / Número de acciones	float
Gross.Profit.to.Sales	Beneficio bruto sobre ventas	Margen bruto	float
Tax.rate..A.	Tasa impositiva (A)	Tasa efectiva de impuestos	float
Current.Liability.to.Liability	Pasivo corriente / Pasivo total	Proporción de deuda de corto plazo	float
Continuous.Net.Profit.Growth.Rate	Tasa de crecimiento continuo de la utilidad neta	Crecimiento neto interanual	float
Long.term.Liability.to.Current.Assets	Pasivo a largo plazo / Activos corrientes	Indicador de solvencia	float
Total.assets.to.GNP.price	Activos totales sobre precio del PIB	Relación entre activos y economía nacional	float
Current.Liabilities.Equity	Pasivo corriente / Patrimonio	Proporción de deuda de corto plazo sobre capital	float
CFO.to.Assets	Flujo operativo / Activos totales	Eficiencia en generación de flujo	float
Current.Liability.to.Current.Assets	Pasivo corriente / Activos corrientes	Índice de liquidez corriente	float
Persistent.EPS.in.the.Last.Four.Seasons	EPS persistente en los últimos 4 trimestres	Consistencia de las ganancias	float
Cash.Total.Assets	Efectivo / Activos totales	Nivel de liquidez	float
Inventory.Working.Capital	Inventario / Capital de trabajo	Proporción del inventario dentro del capital disponible	float
Net.Value.Per.Share..B.	Valor neto por acción (B)	Versión B del valor contable por acción	float
Current.Assets.Total.Assets	Activos corrientes / Activos totales	Proporción de activos líquidos	float
Net.Worth.Turnover.Rate..times.	Rotación del patrimonio neto	Ventas / Patrimonio neto	float
Interest.Coverage.Ratio..Interest.expense.to.EBIT.	Cobertura de intereses	Capacidad para pagar intereses	float
Equity.to.Liability	Patrimonio / Pasivo	Apalancamiento	float
Operating.profit.Paid.in.capital	Beneficio operativo / Capital pagado	Retorno sobre capital	float
No.credit.Interval	Intervalo sin crédito	Tiempo en que la empresa no accedió a créditos	float
Interest.bearing.debt.interest.rate	Tasa de interés de deuda con interés	Costo del financiamiento	float
Net.Income.to.Stockholder.s.Equity	Utilidad neta / Patrimonio del accionista	Rentabilidad sobre capital (ROE)	float
Quick.Ratio	Razón rápida	Liquidez sin inventario	float
Cash.Flow.to.Sales	Flujo de caja / Ventas	Conversión de ventas en efectivo	float
Equity.to.Long.term.Liability	Patrimonio / Pasivo de largo plazo	Cobertura del capital ante deudas largas	float
Working.Capital.Equity	Capital de trabajo / Patrimonio	Liquidez frente al capital	float
After.tax.net.Interest.Rate	Rentabilidad neta después de impuestos	Ganancia neta final	float
Current.Liability.to.Assets	Pasivo corriente / Activos totales	Presión de deuda de corto plazo	float
Net.Value.Per.Share..C.	Valor neto por acción (C)	Otra versión de valor contable por acción	float
Revenue.per.person	Ingresos por persona	Eficiencia del personal	float
Borrowing.dependency	Dependencia de préstamos	Proporción de financiación externa	float
Operating.Profit.Rate	Tasa de beneficio operativo	Rentabilidad operativa	float
Long.term.fund.suitability.ratio..A.	Índice de adecuación de fondos a largo plazo (A)	¿Fondos de largo plazo cubren activos largos?	float
Pre.tax.net.Interest.Rate	Rentabilidad antes de impuestos	Rentabilidad sin impuestos	float
After.tax.Net.Profit.Growth.Rate	Tasa de crecimiento de la utilidad neta (post impuestos)	Medida de crecimiento real	float
Operating.profit.per.person	Beneficio operativo por persona	Productividad por empleado	float
Realized.Sales.Gross.Margin	Margen bruto sobre ventas realizadas	% de ganancia bruta	float
Cash.Current.Liability	Efectivo / Pasivo corriente	Liquidez inmediata	float
Current.Liability.to.Equity	Pasivo corriente / Patrimonio	Riesgo de corto plazo	float
Total.expense.Assets	Gasto total / Activos	Costo relativo de operación	float
Current.Asset.Turnover.Rate	Rotación de activos corrientes	Eficiencia en uso de activos	float
Fixed.Assets.Turnover.Frequency	Frecuencia de rotación de activos fijos	Intensidad de uso de maquinaria y propiedades	float
ROA.B..before.interest.and.depreciation.after.tax	Rentabilidad sobre activos antes de intereses y depreciación, después de impuestos	Versión de ROA ajustada	float
Quick.Asset.Turnover.Rate	Rotación de activos líquidos	Qué tan rápido se usan activos fácilmente convertibles	float
Debt.ratio..	Razón de endeudamiento	Pasivo / Activos totales	float
Retained.Earnings.to.Total.Assets	Utilidades retenidas / Activos totales	Reinversión	float
Total.debt.Total.net.worth	Deuda total / Patrimonio neto	Apalancamiento	float
Fixed.Assets.to.Assets	Activos fijos / Activos totales	Proporción de activos no líquidos	float
Total.Asset.Growth.Rate	Tasa de crecimiento de activos totales	Expansión de la empresa	float
Inventory.and.accounts.receivable.Net.value	Valor neto de inventario y cuentas por cobrar	Activos circulantes clave	float
Net.Income.Flag	Indicador binario de ingreso neto	Flag de ganancia positiva	int
Per.Share.Net.profit.before.tax..Yuan...	Utilidad neta por acción antes de impuestos (en Yuanes)	Ganancia previa a impuestos por acción	float
Continuous.interest.rate..after.tax.	Tasa continua de interés neto después de impuestos	Rentabilidad constante ajustada	float
Liability.Assets.Flag	Indicador binario de pasivo sobre activos	¿Relación riesgo-alto?	int
Working.Capital.to.Total.Assets	Capital de trabajo / Activos totales	Liquidez general	float
Degree.of.Financial.Leverage..DFL.	Grado de apalancamiento financiero	Qué tanto se depende de deuda para generar beneficios	float
Operating.Gross.Margin	Margen bruto operativo	Rentabilidad sin considerar impuestos y financieros	float
Contingent.liabilities.Net.worth	Pasivos contingentes / Patrimonio neto	Riesgo legal/potencial	float
Operating.Profit.Growth.Rate	Tasa de crecimiento del beneficio operativo	Evolución operativa	float
Cash.Flow.to.Liability	Flujo de efectivo / Pasivo total	Capacidad de pago	float
Quick.Assets.Total.Assets	Activos líquidos / Activos totales	Liquidez inmediata frente al total del negocio	float

	count	mean	std	min	25%	50%	75%	max
ID	2050.0	1.276675e+03	7.337705e+02	1.000000	645.250000	1286.500000	1.902750e+03	2.550000e+03
Bankruptcy	2050.0	3.024390e-02	1.712995e-01	0.000000	0.000000	0.000000	0.000000e+00	1.000000e+00
Realized.Sales.Gross.Profit.Growth.Rate	2050.0	5.242644e-02	3.734597e-02	0.005576	0.022660	0.039844	7.413378e-02	1.976222e-01
Operating.Expense.Rate	2050.0	1.143592e+10	6.637064e+10	0.000018	0.000387	0.002891	2.040574e+06	8.153957e+11
Operating.Profit.Per.Share..Yuan...	2050.0	1.684231e-01	7.482216e-02	0.026690	0.107202	0.153541	2.197845e-01	4.024868e-01
...	...	...	...	...	...	...	...	...
Operating.Gross.Margin	2050.0	6.595301e-01	6.430054e-02	0.449928	0.608820	0.654584	7.120428e-01	8.083387e-01
Contingent.liabilities.Net.worth	2050.0	2.062910e-02	1.928225e-02	0.001931	0.005786	0.012352	2.978119e-02	9.671401e-02
Operating.Profit.Growth.Rate	2050.0	8.707365e-01	2.806991e-02	0.808653	0.848945	0.869933	8.936929e-01	9.212760e-01
Cash.Flow.to.Liability	2050.0	5.284203e-01	8.417075e-02	0.078139	0.462781	0.519719	5.964921e-01	1.000000e+00
Quick.Assets.Total.Assets	2050.0	4.621027e-01	2.114766e-01	0.004725	0.295769	0.458141	6.208409e-01	9.896206e-01

	Train	Validation
Loss	0.0	0.27
Accuracy	1.0	0.97
Precision	1.0	0.38
Recall	1.0	0.25
AUC	1.0	0.69
F1-score	1.0	0.30

	Train	Validation
Loss	0.09	0.11
Accuracy	0.98	0.97
Precision	1.00	0.50
Recall	0.22	0.08
AUC	0.95	0.92
F1-score	0.36	0.14

	Train	Validation
Loss	0.13	0.12
Accuracy	0.97	0.97
Precision	0.25	0.00
Recall	0.02	0.00
AUC	0.86	0.91
F1-score	0.04	0.00

	Train	Validation
Loss	0.10	0.11
Accuracy	0.97	0.97
Precision	0.82	0.00
Recall	0.18	0.00
AUC	0.92	0.92
F1-score	0.30	0.00

	Train	Validation
Loss	0.30	0.35
Accuracy	0.92	0.88
Precision	0.88	0.16
Recall	0.96	0.75
AUC	0.96	0.93
F1-score	0.92	0.26

	Train	Validation
Loss	0.31	0.38
Accuracy	0.92	0.90
Precision	0.89	0.20
Recall	0.97	0.75
AUC	0.97	0.89
F1-score	0.93	0.32

Predicción de bancarrota empresarial¶

Importación de librerías¶

1. Exploración de los datos¶

Cargue de datos¶

Información general¶

Tamaño y visualización de los datasets¶

Tipos de datos¶

Estadístias descriptivas¶

Histogramas¶

Detección de valores faltantes y duplicados¶

Valores atípicos¶

Correlaciones¶

Correlaciones entre variables predictoras¶

Conclusiones etapa 1: Exploración¶

2. Preparación de los datos¶

Ingeniería de variables¶

Verificación de valores vacíos¶

Imputación de valores vacíos: Median imputer¶

División en datos de entrenamiento y validación¶

Normalización de los datos¶

Balanceo: SMOTE¶

Selección de variables¶

1. Análisis de Componentes Principales (PCA)¶

2. Importancia de las variables mediante XGBoost Classifier¶

Obtención de datasets para desarrollo de modelos¶

1. Obtención dataset de selección de variables XGBOOST sin balanceo¶

2. Obtención dataset de selección de variables XGBOOST con balanceo (SMOTE)¶

3. Obtención dataset de selección de variables PCA sin balanceo¶

4. Obtención dataset de selección de variables PCA con balanceo (SMOTE)¶

Conclusiones etapa 2: Preparación de los datos¶

3. Análisis preliminar de selección de modelos¶

4. Desarrollo y calibración de modelos¶

Diseño de la arquitectura de la red MLP y compilación del modelo: Modelo base¶

Modelo base + Incorporación de técnicas de regularización¶

Calibración de hiperparámetros manual¶

Mejor modelo ajuste de hiperparámetros manual¶

Calibración de hiperparámetros en Keras¶

1. Randomized search¶

2. Grid search¶

3. Keras turner¶

3.1 Entrenamiento con dataset de selección de variables XGBOOST sin balanceo¶

3.2 Entrenamiento con dataset de selección de variables XGBOOST con balanceo (SMOTE)¶

3.3 Entrenamiento con dataset de selección de variables PCA sin balanceo¶

3.4 Entrenamiento con dataset de selección de variables PCA con balanceo (SMOTE)¶

5. Visualización de resultados¶

Bibliografía¶